Improving Tool Calling Success Rates for Small Models
newz2000 · reddit · 2026-07-18
This detailed Reddit post summarizes how the author managed to improve the success rate of small models calling tools. The core findings are highly practical and engineering-focused.
The author first had Claude write a test script using mock tools and ollama to run various models and parameter combinations, observing whether tool calls hit the expected targets. Initial results showed high failure rates for small models, so they continued iterating parameters with Claude until achieving a 100% success rate.
Key takeaways:
- More tools lead to more errors: The larger the toolset, the harder it is for the model to decide which one to call. Thus, merging multiple tools into fewer, higher-level tools is better.
- Don't make parameter constraints too strict: Overly strict parameter validation makes it harder for small models to comply.
- Be specific with error prompts: Instead of generic failure messages, explicitly telling the model which parameter is missing is much more effective.
They also shared a ClickUp example: after condensing roughly 100 tools down to 5 local tools, the token context dropped from around 50k to just a few thousand, the API call chain became faster, and overall execution time was reduced to about 10% of the original.
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